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2020 Vol.62, Issue 1 Preview Page
Comparison of Regression Models for Estimating Ventilation Rate of Mechanically Ventilated Swine Farm

강제환기식 돈사의 환기량 추정을 위한 회귀모델의 비교

Gwanggon Joa
,
Taehwan Hab
,
Sanghoo Yoonc
,
Yuna Jangd
,
Minwoong Junge†
조 광곤a
,
하 태환b
,
윤 상후c
,
장 유나d
,
정 민웅e†
aResearcher, Division of Animal Environment, National Institute of Animal Science (NIAS)
bResearcher, Division of Animal Environment, National Institute of Animal Science (NIAS)
cAssistant Professor, Division of Mathematics and Big Data Science, Daegu University
dResearcher, Division of Animal Environment, National Institute of Animal Science (NIAS)
eSenior researcher, Division of Animal Environment, National Institute of Animal Science (NIAS)
a국립 동물 과학원 동물 환경과 연구원
b국립 동물 과학원 동물 환경과 연구원
c대구 대학교 수학 빅 데이터과 부교수
d국립 동물 과학원 동물 환경과 연구원
e국립 동물 과학원 동물 환경과 선임 연구원
† Corresponding author Tel.: +82-63-238-7411, Fax: +82-63-238-7447 E-mail: mwjung@korea.kr
2020. pp. 61-70
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Abstract

To estimate the ventilation volume of mechanically ventilated swine farms, various regression models were applied, and errors were compared to select the regression model that can best simulate actual data. Linear regression, linear spline, polynomial regression (degrees 2 and 3), logistic curve, generalized additive model (GAM), and gompertz curve were compared. Overfitting models were excluded even when the error rate was small. The evaluation criteria were root mean square error (RMSE) and mean absolute percentage error (MAPE). The evaluation results indicated that degree 3 exhibited the lowest error rate; however, an overestimation contradiction was observed in a certain section. The logistic curve was the most stable and superior to all the models. In the estimation of ventilation volume by all of the models, the estimated ventilation volume of the logistic curve was the smallest except for the model with a large error rate and the overestimated model.

Keywords
Swine farm
mechanical ventilation
regression
logistic curve
generalized additive model
gompertz statistical model
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Information
  • Publisher :The Korean Society of Agricultural Engineers
  • Publisher(Ko) :한국농공학회
  • Journal Title :Journal of Korean Society of Agricultural Engineers
  • Journal Title(Ko) :한국농공학회논문집
  • Volume : 62
  • No :1
  • Pages :61-70
  • Received Date : 2019-11-20
  • Revised Date : 2019-11-29
  • Accepted Date : 2019-12-02
  • DOI :https://doi.org/10.5389/KSAE.2020.62.1.061
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